Thesis Abstract

Abstract
This thesis explores the application of predictive modeling techniques for credit risk assessment in the context of commercial banking. The study aims to develop a robust predictive model that can enhance the accuracy and efficiency of credit risk assessment processes in commercial banks. The research focuses on leveraging historical data, machine learning algorithms, and statistical analysis to predict the likelihood of default and assess creditworthiness of borrowers. The introduction provides an overview of the background of the study, highlighting the importance of credit risk assessment in banking operations and the challenges faced by banks in accurately evaluating credit risk. The problem statement identifies the gaps in existing credit risk assessment methods and the need for more sophisticated predictive modeling techniques to improve risk management practices in commercial banking. The objectives of the study include developing a predictive model for credit risk assessment, evaluating the performance of the model against traditional methods, and assessing the impact of the model on credit risk management practices in commercial banks. The limitations of the study are discussed, acknowledging constraints such as data availability, model complexity, and external factors that may impact the accuracy of the predictive model. The scope of the study outlines the specific focus areas and variables considered in developing the predictive model, including borrower characteristics, financial indicators, macroeconomic factors, and industry-specific variables. The significance of the study lies in its potential to enhance credit risk assessment practices, improve decision-making processes, and mitigate credit losses for commercial banks. The structure of the thesis is presented, highlighting the organization of the chapters and the flow of the research work. Chapter One introduces the research topic, provides the background, problem statement, objectives, limitations, scope, significance, and defines key terms relevant to the study. Chapter Two presents a comprehensive literature review on credit risk assessment, predictive modeling techniques, machine learning algorithms, and applications in commercial banking. Chapter Three details the research methodology, including data collection methods, variable selection, model development, validation techniques, and performance evaluation metrics. The chapter also discusses ethical considerations and limitations of the research methodology. Chapter Four presents the findings of the study, including the performance of the predictive model, key insights derived from the analysis, and implications for credit risk management in commercial banking. Chapter Five concludes the thesis by summarizing the key findings, discussing the implications for theory and practice, highlighting the contributions of the study to the field of credit risk assessment, and providing recommendations for future research. Overall, this thesis aims to contribute to the advancement of credit risk management practices in commercial banking through the development and application of predictive modeling techniques.

Thesis Overview

The project titled "Predictive modeling for credit risk assessment in commercial banking" aims to investigate and develop predictive models that can enhance the credit risk assessment process within the commercial banking sector. Credit risk assessment is a crucial aspect of banking operations, as it involves evaluating the likelihood of a borrower defaulting on a loan or credit agreement. By accurately predicting credit risk, banks can make informed decisions regarding lending and mitigate potential financial losses. The research will delve into the current methods and tools used in credit risk assessment within commercial banking, highlighting their strengths and limitations. It will explore the challenges faced by banks in accurately assessing credit risk, such as data quality issues, changing economic conditions, and regulatory requirements. The project will then propose the development of predictive modeling techniques as a solution to improve the accuracy and efficiency of credit risk assessment. The project will involve collecting and analyzing historical loan data, financial information, and other relevant variables to train the predictive models. Various machine learning algorithms, such as logistic regression, decision trees, and neural networks, will be explored and compared to determine the most effective approach for credit risk prediction. The research will also consider the incorporation of alternative data sources, such as social media data and transactional data, to enhance the predictive power of the models. Furthermore, the research will address the interpretability and explainability of the predictive models to ensure that banks can understand and trust the model outputs. Ethical considerations regarding data privacy and fairness in credit risk assessment will also be examined to ensure that the models are developed and deployed responsibly. Overall, the project on "Predictive modeling for credit risk assessment in commercial banking" seeks to contribute to the advancement of credit risk assessment practices in the banking sector by leveraging predictive modeling techniques to improve decision-making processes, reduce risks, and enhance the overall stability and sustainability of commercial banks."